http://giapjournals.com/index.php/ijsrtm <p>International Journal of Students' Research in Technology & Management [eISSN 2321-2543] is a special journal of its kind in publishing the original research and review articles of early researchers of technology & management domain.</p> en-US <p>Authors retain copyright for the published content.</p> [email protected] (Dr Jenny Hope) [email protected] (Mr David Green) Thu, 08 Aug 2019 03:39:45 +0000 OJS 3.1.2.1 http://blogs.law.harvard.edu/tech/rss 60 http://giapjournals.com/index.php/ijsrtm/article/view/ijsrtm.2019.721 <p><strong>Purpose of the study: </strong>The quasi-Monte Carlo method is an important tool for modelling and analysing various complex problems in engineering, physical sciences, finance and business. The crucial element of the method is a sequence of deterministic quasi-random values, which is often obtained by using the Sobol’ quasi-random generator. The purpose of this study is to consider the time complexity of generating the Sobol’ sequence.</p> <p><strong>Methodology: </strong>Algorithms for determining the Sobol’ sequence have been studied. The algorithms have been implemented in the Python programming language.</p> <p><strong>Main Findings: </strong>It is established that this sequence can be generated in the linear time provided that generated numbers are based on 32-bit or 64-bit integers. The main result of the paper is the algorithm which enables this time-bound.</p> <p><strong>Applications of this study: </strong>The study can be applied in engineering, physical sciences, finance and business.</p> <p><strong>Novelty/Originality of this study:</strong> It is shown that Sobol’ sequence can be generated in linear time.<strong>    </strong></p> Timotej Vesel Copyright (c) 2019 Timotej Vesel http://creativecommons.org/licenses/by-nc-sa/4.0 http://giapjournals.com/index.php/ijsrtm/article/view/ijsrtm.2019.721 Thu, 08 Aug 2019 00:00:00 +0000 http://giapjournals.com/index.php/ijsrtm/article/view/ijsrtm.2019.722 <p><strong>Background:</strong> For a long time, there has been a trend of trading of shares. Brokerage firms and dealers buy/sell stocks for clients and companies. Their work is based on knowing how the share price of the company will react in the market. Market/ share price predictions are useful as the investor/broker can attempt to predict the output in order to maximize his dividends or minimize his losses.</p> <p><strong>Methodology:</strong> R and Python tools are used to sort, segregate and process the data, and techniques/algorithms such as Genetic Algorithm, ARIMA, Artificial Neural Networks, and Linear Regression are used to forecast results of data. Along with the model data, external factors affecting share prices also be taken into account.</p> <p><strong>Findings:</strong> For each of the applied algorithms, their results are compared and the difference in output with the real-time values has been observed and recorded.</p> <p><strong>Implications:</strong> Using data mining techniques, an attempt is made to estimate a prediction model to help forecast share prices.</p> Vignesh Ganesan Copyright (c) 2019 Vignesh Ganesan http://creativecommons.org/licenses/by-nc-sa/4.0 http://giapjournals.com/index.php/ijsrtm/article/view/ijsrtm.2019.722 Tue, 03 Sep 2019 07:00:22 +0000